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Brychta RJ, McGehee S, Huang S, Leitner BP, Duckworth CJ, Fletcher LA, Kim K, Cassimatis TM, Israni NS, Lea HJ, Lentz TN, Pierce AE, Jiang A, LaMunion SR, Thomas RJ, Ishihara A, Courville AB, Yang SB, Reitman ML, Cypess AM, Chen KY. The thermoneutral zone in women takes an "arctic" shift compared to men. Proc Natl Acad Sci U S A 2024; 121:e2311116121. [PMID: 38683977 PMCID: PMC11087792 DOI: 10.1073/pnas.2311116121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 03/05/2024] [Indexed: 05/02/2024] Open
Abstract
Conventionally, women are perceived to feel colder than men, but controlled comparisons are sparse. We measured the response of healthy, lean, young women and men to a range of ambient temperatures typical of the daily environment (17 to 31 °C). The Scholander model of thermoregulation defines the lower critical temperature as threshold of the thermoneutral zone, below which additional heat production is required to defend core body temperature. This parameter can be used to characterize the thermoregulatory phenotypes of endotherms on a spectrum from "arctic" to "tropical." We found that women had a cooler lower critical temperature (mean ± SD: 21.9 ± 1.3 °C vs. 22.9 ± 1.2 °C, P = 0.047), resembling an "arctic" shift compared to men. The more arctic profile of women was predominantly driven by higher insulation associated with more body fat compared to men, countering the lower basal metabolic rate associated with their smaller body size, which typically favors a "tropical" shift. We did not detect sex-based differences in secondary measures of thermoregulation including brown adipose tissue glucose uptake, muscle electrical activity, skin temperatures, cold-induced thermogenesis, or self-reported thermal comfort. In conclusion, the principal contributors to individual differences in human thermoregulation are physical attributes, including body size and composition, which may be partly mediated by sex.
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Affiliation(s)
- Robert J. Brychta
- Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Bethesda, MD20892
| | - Suzanne McGehee
- Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Bethesda, MD20892
| | - Shan Huang
- Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Bethesda, MD20892
| | - Brooks P. Leitner
- Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Bethesda, MD20892
| | - Courtney J. Duckworth
- Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Bethesda, MD20892
| | - Laura A. Fletcher
- Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Bethesda, MD20892
| | - Katherine Kim
- Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Bethesda, MD20892
| | - Thomas M. Cassimatis
- Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Bethesda, MD20892
| | - Nikita S. Israni
- Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Bethesda, MD20892
| | - Hannah J. Lea
- Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Bethesda, MD20892
| | - Taylor N. Lentz
- Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Bethesda, MD20892
| | - Anne E. Pierce
- Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Bethesda, MD20892
| | - Alex Jiang
- Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Bethesda, MD20892
| | - Samuel R. LaMunion
- Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Bethesda, MD20892
| | - Reed J. Thomas
- Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Bethesda, MD20892
| | - Asuka Ishihara
- Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Bethesda, MD20892
| | - Amber B. Courville
- Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Bethesda, MD20892
| | - Shanna B. Yang
- Nutrition Department, Hatfield Clinical Research Center, NIH, Bethesda, MD20892
| | - Marc L. Reitman
- Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Bethesda, MD20892
| | - Aaron M. Cypess
- Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Bethesda, MD20892
| | - Kong Y. Chen
- Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Bethesda, MD20892
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Walitt B, Singh K, LaMunion SR, Hallett M, Jacobson S, Chen K, Enose-Akahata Y, Apps R, Barb JJ, Bedard P, Brychta RJ, Buckley AW, Burbelo PD, Calco B, Cathay B, Chen L, Chigurupati S, Chen J, Cheung F, Chin LMK, Coleman BW, Courville AB, Deming MS, Drinkard B, Feng LR, Ferrucci L, Gabel SA, Gavin A, Goldstein DS, Hassanzadeh S, Horan SC, Horovitz SG, Johnson KR, Govan AJ, Knutson KM, Kreskow JD, Levin M, Lyons JJ, Madian N, Malik N, Mammen AL, McCulloch JA, McGurrin PM, Milner JD, Moaddel R, Mueller GA, Mukherjee A, Muñoz-Braceras S, Norato G, Pak K, Pinal-Fernandez I, Popa T, Reoma LB, Sack MN, Safavi F, Saligan LN, Sellers BA, Sinclair S, Smith B, Snow J, Solin S, Stussman BJ, Trinchieri G, Turner SA, Vetter CS, Vial F, Vizioli C, Williams A, Yang SB, Nath A. Deep phenotyping of post-infectious myalgic encephalomyelitis/chronic fatigue syndrome. Nat Commun 2024; 15:907. [PMID: 38383456 PMCID: PMC10881493 DOI: 10.1038/s41467-024-45107-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Accepted: 01/16/2024] [Indexed: 02/23/2024] Open
Abstract
Post-infectious myalgic encephalomyelitis/chronic fatigue syndrome (PI-ME/CFS) is a disabling disorder, yet the clinical phenotype is poorly defined, the pathophysiology is unknown, and no disease-modifying treatments are available. We used rigorous criteria to recruit PI-ME/CFS participants with matched controls to conduct deep phenotyping. Among the many physical and cognitive complaints, one defining feature of PI-ME/CFS was an alteration of effort preference, rather than physical or central fatigue, due to dysfunction of integrative brain regions potentially associated with central catechol pathway dysregulation, with consequences on autonomic functioning and physical conditioning. Immune profiling suggested chronic antigenic stimulation with increase in naïve and decrease in switched memory B-cells. Alterations in gene expression profiles of peripheral blood mononuclear cells and metabolic pathways were consistent with cellular phenotypic studies and demonstrated differences according to sex. Together these clinical abnormalities and biomarker differences provide unique insight into the underlying pathophysiology of PI-ME/CFS, which may guide future intervention.
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Affiliation(s)
- Brian Walitt
- National Institute of Neurological Diseases and Stroke (NINDS), Bethesda, MD, USA
| | - Komudi Singh
- National Heart, Lung and Blood Institute (NHLBI), Bethesda, MD, USA
| | - Samuel R LaMunion
- National Institute of Diabetes, Digestion, and Kidney Disease (NIDDK), Bethesda, MD, USA
| | - Mark Hallett
- National Institute of Neurological Diseases and Stroke (NINDS), Bethesda, MD, USA
| | - Steve Jacobson
- National Institute of Neurological Diseases and Stroke (NINDS), Bethesda, MD, USA
| | - Kong Chen
- National Institute of Diabetes, Digestion, and Kidney Disease (NIDDK), Bethesda, MD, USA
| | | | - Richard Apps
- NIH Center for Human Immunology, Autoimmunity, and Inflammation (CHI), Bethesda, MD, USA
| | | | - Patrick Bedard
- National Institute of Neurological Diseases and Stroke (NINDS), Bethesda, MD, USA
| | - Robert J Brychta
- National Institute of Diabetes, Digestion, and Kidney Disease (NIDDK), Bethesda, MD, USA
| | | | - Peter D Burbelo
- National Institute of Dental and Craniofacial Research (NIDCR), Bethesda, MD, USA
| | - Brice Calco
- National Institute of Neurological Diseases and Stroke (NINDS), Bethesda, MD, USA
| | - Brianna Cathay
- Texas A&M School of Engineering Medicine, College Station, TX, USA
| | - Li Chen
- Affiliated Hospital of North Sichuan Medical College, Sichuan, China
| | - Snigdha Chigurupati
- George Washington University Hospital, District of Columbia, Washington, DC, USA
| | - Jinguo Chen
- NIH Center for Human Immunology, Autoimmunity, and Inflammation (CHI), Bethesda, MD, USA
| | - Foo Cheung
- NIH Center for Human Immunology, Autoimmunity, and Inflammation (CHI), Bethesda, MD, USA
| | | | | | - Amber B Courville
- National Institute of Diabetes, Digestion, and Kidney Disease (NIDDK), Bethesda, MD, USA
| | | | | | | | | | - Scott A Gabel
- National Institute of Environmental Health Sciences (NIEHS), Chapel Hill, NC, USA
| | - Angelique Gavin
- National Institute of Neurological Diseases and Stroke (NINDS), Bethesda, MD, USA
| | - David S Goldstein
- National Institute of Neurological Diseases and Stroke (NINDS), Bethesda, MD, USA
| | | | - Sean C Horan
- Sidney Kimmel Medical College, Philadelphia, PA, USA
| | - Silvina G Horovitz
- National Institute of Neurological Diseases and Stroke (NINDS), Bethesda, MD, USA
| | - Kory R Johnson
- National Institute of Neurological Diseases and Stroke (NINDS), Bethesda, MD, USA
| | - Anita Jones Govan
- National Institute of Neurological Diseases and Stroke (NINDS), Bethesda, MD, USA
| | - Kristine M Knutson
- National Institute of Neurological Diseases and Stroke (NINDS), Bethesda, MD, USA
| | - Joy D Kreskow
- National Institute of Nursing Research (NINR), Bethesda, MD, USA
| | - Mark Levin
- National Heart, Lung and Blood Institute (NHLBI), Bethesda, MD, USA
| | - Jonathan J Lyons
- National Institute of Allergy and Infectious Disease (NIAID), Bethesda, MD, USA
| | - Nicholas Madian
- National Center for Complementary and Integrative Health (NCCIH), Bethesda, MD, USA
| | - Nasir Malik
- National Institute of Neurological Diseases and Stroke (NINDS), Bethesda, MD, USA
| | - Andrew L Mammen
- National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), Bethesda, MD, USA
| | | | - Patrick M McGurrin
- National Institute of Neurological Diseases and Stroke (NINDS), Bethesda, MD, USA
| | | | - Ruin Moaddel
- National Institute of Aging (NIA), Baltimore, MD, USA
| | - Geoffrey A Mueller
- National Institute of Environmental Health Sciences (NIEHS), Chapel Hill, NC, USA
| | - Amrita Mukherjee
- NIH Center for Human Immunology, Autoimmunity, and Inflammation (CHI), Bethesda, MD, USA
| | - Sandra Muñoz-Braceras
- National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), Bethesda, MD, USA
| | - Gina Norato
- National Institute of Neurological Diseases and Stroke (NINDS), Bethesda, MD, USA
| | - Katherine Pak
- National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), Bethesda, MD, USA
| | - Iago Pinal-Fernandez
- National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), Bethesda, MD, USA
| | - Traian Popa
- National Institute of Neurological Diseases and Stroke (NINDS), Bethesda, MD, USA
| | - Lauren B Reoma
- National Institute of Neurological Diseases and Stroke (NINDS), Bethesda, MD, USA
| | - Michael N Sack
- National Heart, Lung and Blood Institute (NHLBI), Bethesda, MD, USA
| | - Farinaz Safavi
- National Institute of Neurological Diseases and Stroke (NINDS), Bethesda, MD, USA
- National Institute of Allergy and Infectious Disease (NIAID), Bethesda, MD, USA
| | - Leorey N Saligan
- National Institute of Nursing Research (NINR), Bethesda, MD, USA
| | - Brian A Sellers
- NIH Center for Human Immunology, Autoimmunity, and Inflammation (CHI), Bethesda, MD, USA
| | | | - Bryan Smith
- National Institute of Neurological Diseases and Stroke (NINDS), Bethesda, MD, USA
| | - Joseph Snow
- National Institute of Mental Health (NIMH), Bethesda, MD, USA
| | | | - Barbara J Stussman
- National Institute of Neurological Diseases and Stroke (NINDS), Bethesda, MD, USA
- National Center for Complementary and Integrative Health (NCCIH), Bethesda, MD, USA
| | | | | | | | - Felipe Vial
- Clínica Alemana Universidad del Desarrollo, Santiago, Chile
| | - Carlotta Vizioli
- National Institute of Neurological Diseases and Stroke (NINDS), Bethesda, MD, USA
| | - Ashley Williams
- Oakland University William Beaumont School of Medicine, Rochester, NY, USA
| | | | - Avindra Nath
- National Institute of Neurological Diseases and Stroke (NINDS), Bethesda, MD, USA.
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LaMunion SR, Crouter SE, Broskey NT, Altazan AD, Redman LM. Discrimination of wear and non-wear in infants using data from hip- and ankle-worn devices. PLoS One 2020; 15:e0240604. [PMID: 33137144 PMCID: PMC7605692 DOI: 10.1371/journal.pone.0240604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Accepted: 09/29/2020] [Indexed: 11/20/2022] Open
Abstract
INTRODUCTION A key component to analyzing wearable sensor data is identifying periods of non-wear. Traditionally, strings of consecutive zero counts (e.g. >60-minutes) are identified indicating periods of non-movement. The non-movement window length is then evaluated as wear or non-wear. Given that non-movement is not equivalent to non-wear, additional criteria should be evaluated to objectively identify periods of non-wear. Identifying non-wear is especially challenging in infants due to their sporadic movement, sleep frequency, and proportion of caregiver-generated movement. PURPOSE To use hip- and ankle-worn ActiGraph wGT3X-BT (wGT3X-BT) data to identify non-wear in infants. METHODS Fifteen infant participants [mean±SD; age, 8.7±1.7 weeks (range 5.4-11.3 weeks); 5.1±0.8 kg; 56.2±2.1 cm; n = 8 females] wore a wGT3X-BT on the hip and ankle. Criterion data were collected during two, 2-hour directly observed periods in the laboratory. Using raw 30 Hz acceleration data, a vector magnitude and the inclination angle of each individual axis were calculated before being averaged into 1-minute windows. Three decision tree models were developed using data from 1) hip only, 2) ankle only, and 3) hip and ankle combined. RESULTS The hip model classified 86.6% of all minutes (wear and non-wear) correctly (F1 = 75.5%) compared to the ankle model which classified 90.6% of all minutes correctly (F1 = 83.0%). The combined site model performed similarly to the ankle model and correctly classified 90.0% of all minutes (F1 = 80.8%). CONCLUSION The similar performance between the ankle only model and the combined site model likely indicates that the features from the ankle device are more important for identifying non-wear in infants. Overall, this approach provides an advancement in the identification of device wear status using wearable sensor data in infants.
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Affiliation(s)
- Samuel R. LaMunion
- Department of Kinesiology, Recreation, and Sport Studies, The University of Tennessee, Knoxville, TN, United States of America
| | - Scott E. Crouter
- Department of Kinesiology, Recreation, and Sport Studies, The University of Tennessee, Knoxville, TN, United States of America
| | - Nicholas T. Broskey
- Reproductive Endocrinology and Women’s Health Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA, United States of America
| | - Abby D. Altazan
- Reproductive Endocrinology and Women’s Health Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA, United States of America
| | - Leanne M. Redman
- Reproductive Endocrinology and Women’s Health Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA, United States of America
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LaMunion SR, Crouter SE, Broskey NT, Altazan AD, Redman LM. Identification Of Actigraph Wgt3x-bt Device Non-wear In Infants. Med Sci Sports Exerc 2020. [DOI: 10.1249/01.mss.0000678296.02701.2d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Abstract
PURPOSE The purpose of this study was to assess the accuracy of the Cosmed K5 portable metabolic system dynamic mixing chamber (MC) and breath-by-breath (BxB) modes against the criterion Douglas bag (DB) method. METHODS Fifteen participants (mean age±SD, 30.6±7.4 yrs) had their metabolic variables measured at rest and during cycling at 50, 100, 150, 200, and 250W. During each stage, participants were connected to the first respiratory gas collection method (randomized) for the first four minutes to reach steady state, followed by 3-min (or 5-min for DB) collection periods for the resting condition, and 2-min collection periods for all cycling intensities. Collection periods for the second and third methods were preceded by a washout of 1-3 min. Repeated measures ANOVAs were used to compare metabolic variables measured by each method, for seated rest and each cycling work rate. RESULTS For ventilation (VE) and oxygen uptake (VO2), the K5 MC and BxB modes were within 2.1 l/min (VE) and 0.08 l/min (VO2) of the DB (p≥0.05). Compared to DB values, carbon dioxide production (VCO2) was significantly underestimated by the K5 BxB mode at work rates ≥150W by 0.12-0.31 l/min (p<0.05). K5 MC and BxB respiratory exchange ratio values were significantly lower than DB at cycling work rates ≥100W by 0.03-0.08 (p<0.05). CONCLUSION Compared to the DB method, the K5 MC and BxB modes are acceptable for measuring VE and VO2 across a wide range of cycling intensities. Both K5 modes provided comparable values to each other.
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Affiliation(s)
- Scott E. Crouter
- Department of Kinesiology, Recreation, and Sport Studies, The University of Tennessee Knoxville, Knoxville, TN, United States of America
- * E-mail:
| | - Samuel R. LaMunion
- Department of Kinesiology, Recreation, and Sport Studies, The University of Tennessee Knoxville, Knoxville, TN, United States of America
| | - Paul R. Hibbing
- Department of Kinesiology, Recreation, and Sport Studies, The University of Tennessee Knoxville, Knoxville, TN, United States of America
| | - Andrew S. Kaplan
- Department of Kinesiology, Recreation, and Sport Studies, The University of Tennessee Knoxville, Knoxville, TN, United States of America
| | - David R. Bassett
- Department of Kinesiology, Recreation, and Sport Studies, The University of Tennessee Knoxville, Knoxville, TN, United States of America
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LaMunion SR, Blythe AL, Hibbing PR, Kaplan AS, Clendenin BJ, Crouter SE. Use of consumer monitors for estimating energy expenditure in youth. Appl Physiol Nutr Metab 2019; 45:161-168. [PMID: 31269409 DOI: 10.1139/apnm-2019-0129] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
The purpose of this study was to compare energy expenditure (EE) estimates from 5 consumer physical activity monitors (PAMs) to indirect calorimetry in a sample of youth. Eighty-nine youth (mean (SD); age, 12.3 (3.4) years; 50% female) performed 16 semi-structured activities. Activities were performed in duplicate across 2 visits. Participants wore a Cosmed K4b2 (criterion for EE), an Apple Watch 2 (left wrist), Mymo Tracker (right hip), and Misfit Shine 2 devices (right hip; right shoe). Participants were randomized to wear a Samsung Gear Fit 2 or a Fitbit Charge 2 on the right wrist. Oxygen consumption was converted to EE by subtracting estimated basal EE (Schofield's equation) from the measured gross EE. EE from each visit was summed across the 2 visit days for comparison with the total EE recorded from the PAMs. All consumer PAMs estimated gross EE, except for the Apple Watch 2 (net Active EE). Paired t tests were used to assess differences between estimated (PAM) and measured (K4b2) EE. Mean absolute percent error (MAPE) was used to assess individual-level error. The Mymo Tracker was not significantly different from measured EE and was within 15.9 kcal of measured kilocalories (p = 0.764). Mean percent errors ranged from 3.5% (Mymo Tracker) to 48.2% (Apple Watch 2). MAPE ranged from 16.8% (Misfit Shine 2 - right hip) to 49.9% (Mymo Tracker). Novelty Only the Mymo Tracker was not significantly different from measured EE but had the greatest individual error. The Misfit Shine 2 - right hip had the lowest individual error. Caution is warranted when using consumer PAMs in youth for tracking EE.
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Affiliation(s)
- Samuel R LaMunion
- Department of Kinesiology, Recreation, and Sport Studies, The University of Tennessee, Knoxville, TN 37996, USA
| | - Andrew L Blythe
- Department of Kinesiology, Recreation, and Sport Studies, The University of Tennessee, Knoxville, TN 37996, USA
| | - Paul R Hibbing
- Department of Kinesiology, Recreation, and Sport Studies, The University of Tennessee, Knoxville, TN 37996, USA
| | - Andrew S Kaplan
- Department of Kinesiology, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA
| | - Brandon J Clendenin
- Department of Kinesiology, Recreation, and Sport Studies, The University of Tennessee, Knoxville, TN 37996, USA
| | - Scott E Crouter
- Department of Kinesiology, Recreation, and Sport Studies, The University of Tennessee, Knoxville, TN 37996, USA
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Parr BB, LaMunion SR, Jolley AP, Hatchett AG. Relationship Between Body Mass Index, Core Strength, and Balance in Adults. Med Sci Sports Exerc 2018. [DOI: 10.1249/01.mss.0000536432.73531.71] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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LaMunion SR, Hibbing PR, Kaplan AS, Crouter SE. Physical Activity Category Classification Using The Actigraph Gt9x In Youth. Med Sci Sports Exerc 2018. [DOI: 10.1249/01.mss.0000536057.74878.e1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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LaMunion SR, Bassett DR, Toth LP, Crouter SE. Effect of Wear Location on ActiGraph Activity Counts. Med Sci Sports Exerc 2017. [DOI: 10.1249/01.mss.0000518693.36595.09] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Kaplan AS, LaMunion SR, Bassett DR, Crouter SE. Improved Count Based Metrics For Estimation Of Energy Expenditure With Waist Worn Actigraph. Med Sci Sports Exerc 2017. [DOI: 10.1249/01.mss.0000518702.95798.95] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Toth LP, Bassett DR, Crouter SE, Overstreet BS, LaMunion SR, Park S, Notta SN, Springer CM. StepWatch accuracy during walking, running, and intermittent activities. Gait Posture 2017; 52:165-170. [PMID: 27914311 DOI: 10.1016/j.gaitpost.2016.11.035] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2016] [Revised: 11/01/2016] [Accepted: 11/20/2016] [Indexed: 02/02/2023]
Abstract
INTRODUCTION The purpose of this study was two-fold: 1) to investigate effects of cadence and sensitivity settings for the StepWatch (SW3) on step count accuracy over a wide range of ambulatory speeds, and 2) to compare the preprogrammed "quick start" settings to modified settings during intermittent lifestyle activities. METHODS Part 1: Fifteen participants (18-57 years of age) performed two trials of treadmill walking and running at ten speeds ranging from 26.8 to 268mmin-1 while wearing four SW3 devices. During the first trial, the cadence setting was maintained while sensitivity was varied; in the second trial sensitivity was maintained while the cadence setting was varied. Part 2: Fifteen participants performed four intermittent activities and drove an automobile while wearing two SW3 devices, one with preprogrammed settings and the other with the modified settings determined in Part 1. RESULTS Part 1: The modified settings (cadence setting of 70% of default and sensitivity of 16) provided the greatest step counting accuracy across a wide range of speeds reporting 96.0-104% of actual steps between 53.6 and 268mmin-1. Part 2: The preprogrammed settings tended to have higher accuracy for light household tasks (recording 88% to 94% of actual steps) than the modified settings (recording 82% to 86% of actual steps) which showed a trend towards higher accuracy for tennis (recording 93% vs. 89% of actual steps) (p<0.05). CONCLUSION The preprogrammed "quick start" StepWatch settings should be used with individuals who do not engage in running and vigorous sports. However, for individuals who engage in running and tennis, use of modified settings may result in improved step counting accuracy.
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Affiliation(s)
- Lindsay P Toth
- Department of Kinesiology, Recreation, and Sports Studies, University of Tennessee, Knoxville, TN, United States.
| | - David R Bassett
- Department of Kinesiology, Recreation, and Sports Studies, University of Tennessee, Knoxville, TN, United States
| | - Scott E Crouter
- Department of Kinesiology, Recreation, and Sports Studies, University of Tennessee, Knoxville, TN, United States
| | - Brittany S Overstreet
- Department of Kinesiology and Applied Physiology, University of Delaware, Newark, DE, United States
| | - Samuel R LaMunion
- Department of Kinesiology, Recreation, and Sports Studies, University of Tennessee, Knoxville, TN, United States
| | - Susan Park
- Department of Kinesiology, Recreation, and Sports Studies, University of Tennessee, Knoxville, TN, United States
| | - Shahnawaz N Notta
- Department of Kinesiology, Recreation, and Sports Studies, University of Tennessee, Knoxville, TN, United States
| | - Cary M Springer
- Office of Information Technology, Research Support, University of Tennessee, Knoxville, TN, United States
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LaMunion SR, Celec AN, Burr SD, Parr BB, McLaughlin JE. Accuracy of a Mobile Device Heart Rate Application for Measuring Resting and Exercise Heart Rate. Med Sci Sports Exerc 2014. [DOI: 10.1249/01.mss.0000494074.67659.6b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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